Support-vector machines
Who
Vladimir Vapnik, a Soviet-born Jewish mathematician, with Alexey Chervonenkis and later colleagues at Bell Labs
When
1963–1995
Where
Moscow, USSR, and AT&T Bell Labs, New Jersey, United States
Support-vector machines are a powerful family of machine-learning algorithms for classification and regression. Their theory grew from work begun in the 1960s by Vladimir Vapnik, a mathematician from a Soviet Jewish family, with Alexey Chervonenkis, and was completed in the 1990s after Vapnik moved to AT&T Bell Labs in the U.S. SVMs became one of the most widely used methods in pattern recognition before the deep-learning era.
Sources
Part of “The Gifts of the Jews” — verified Jewish contributions to the world, with sources.